Sustainability Indicators: Relevance, Public Policy Support and Challenges
Bibliographic record
Abstract
Sustainability is a topic that has gained importance in several fields of knowledge, including the public, private and society spheres, based on the discussions that involve the definition of several public policies. Sustainability Indicators (SI) are metrics that seek to measure the level of sustainability and compile information for better decision-making concerning policies, programs, projects and actions related to sustainability. Demonstrated their relevance to public policies the SI appears as an essential tool for evaluating development goals as a sustainable proposal. In this way, this research aimed to discuss the main challenges and methodological limitations found in the use of SI, emphasizing the main fragilities identified in the literature. In methodological terms, the research has exploratory characteristics, supported by the mixed methods approach using a theoretical-empirical analysis, from the available literature on the subject and the methodologies used and the experience of researchers about the topic addressed. The main results demonstrated that Sustainability Indicators are tools that should be used to define, implement, evaluate and monitor public policies at all levels, considering the potentialities/weaknesses and priorities of each context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".